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Looking for a learning/accountability partner on my road to learn a new subject
20th May, 2020
Last date to join: 4th June, 2020
Hi, If you are not into ML, kindly skip to learning/accountability partner section. I have 6 years of experience as a data scientist and have implemented a few Ml algorithms over the years. But at times I feel my theoretical knowledge of the subject is very limited. So starting next week I am planning to start this journey As of now this is my choice of learning path: Linear Algebra - 3blue1brown, Gilbert Strang course Calculus - 3blue1brown. I already have hands on experience in solving calculus problems. So can afford to revise concepts need based Basic ML - Combination of Andrew NG's course, Math heavy Learning from Data, Cornell CS4780, Hands on ML book DL - Haven't decided yet. But mostly andrew NG's course, fastai, and the above book Above course list is not sacrosanct. Can change later while actually learning learning/accountability partner: I am looking for someone who is also planning to learn something new or a course in any field so that both of us can check up on each other frequently and keep each other accountable
Proficient
English
Description
In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
Syllabus
- Week 1: Introduction to Machine Learning
- Welcome to the Machine Learning Specialization! You're joining millions of others who have taken either this or the original course, which led to the founding of Coursera, and has helped millions of other learners, like you, take a look at the exciting world of machine learning!
- Week 2: Regression with multiple input variables
- This week, you'll extend linear regression to handle multiple input features. You'll also learn some methods for improving your model's training and performance, such as vectorization, feature scaling, feature engineering and polynomial regression. At the end of the week, you'll get to practice implementing linear regression in code.
- Week 3: Classification
- This week, you'll learn the other type of supervised learning, classification. You'll learn how to predict categories using the logistic regression model. You'll learn about the problem of overfitting, and how to handle this problem with a method called regularization. You'll get to practice implementing logistic regression with regularization at the end of this week!
Supervised Machine Learning: Regression and Classification
Online Courses 
Coursera
Free to Audit 
1 day 9 hours 19 minutes 
Beginner 
Paid Certificate Get notified about new study groups every week!
Looking for a learning/accountability partner on my road to learn a new subject
20th May, 2020
Last date to join: 4th June, 2020
Affiliate notice
Hi, If you are not into ML, kindly skip to learning/accountability partner section. I have 6 years of experience as a data scientist and have implemented a few Ml algorithms over the years. But at times I feel my theoretical knowledge of the subject is very limited. So starting next week I am planning to start this journey As of now this is my choice of learning path: Linear Algebra - 3blue1brown, Gilbert Strang course Calculus - 3blue1brown. I already have hands on experience in solving calculus problems. So can afford to revise concepts need based Basic ML - Combination of Andrew NG's course, Math heavy Learning from Data, Cornell CS4780, Hands on ML book DL - Haven't decided yet. But mostly andrew NG's course, fastai, and the above book Above course list is not sacrosanct. Can change later while actually learning learning/accountability partner: I am looking for someone who is also planning to learn something new or a course in any field so that both of us can check up on each other frequently and keep each other accountable
Proficient
English
-
TypeOnline Courses
-
ProviderCoursera
-
PricingFree to Audit
-
Duration1 day 9 hours 19 minutes
-
DifficultyBeginner
-
CertificatePaid Certificate
In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
- Week 1: Introduction to Machine Learning
- Welcome to the Machine Learning Specialization! You're joining millions of others who have taken either this or the original course, which led to the founding of Coursera, and has helped millions of other learners, like you, take a look at the exciting world of machine learning!
- Week 2: Regression with multiple input variables
- This week, you'll extend linear regression to handle multiple input features. You'll also learn some methods for improving your model's training and performance, such as vectorization, feature scaling, feature engineering and polynomial regression. At the end of the week, you'll get to practice implementing linear regression in code.
- Week 3: Classification
- This week, you'll learn the other type of supervised learning, classification. You'll learn how to predict categories using the logistic regression model. You'll learn about the problem of overfitting, and how to handle this problem with a method called regularization. You'll get to practice implementing logistic regression with regularization at the end of this week!
Learning is better with Cohorts
Active hands-on learning
Build assignments each week
Feedback loop
Submit your assignment, and receive feedback from your peers. Stuck on a problem?
Learn with a cohort of peers
Join a group of like-minded people who want to learn and grow alongside you
Frequently asked questions
Yes. Our study groups (all of them) are free to join
You join the group and study the MOOC together on a schedule. The exact dates, deadlines, are created by the host
This depends on the host of your group. Some groups have weekly video calls for accountability + doubt solving.
Moocable is a community where you can find study partners, mentors, or people to collaborate on projects. It's designed for people who want to upskill, but struggle with self-learning. Users often post about their skills, goals, and what they're looking to learn or work on, and others can respond to form partnerships or groups. You can join our community
4th June, 2020
Get notified about new study groups every week!
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